
#Importing all the required libraries to run the below code
import pandas as pd
# Creating a dataframe by reading the CSV file onto it using pandas
df = pd.read_csv('Dataset.csv',encoding='latin-1')
# Filter the DataFrame to include only rows where the 'Country' column is 'United Kingdom'
uk_universities = df[df['Location'] == 'United Kingdom']
# Printing the dataframe created, data printed is pre-processed
uk_universities.head()

#In the above code df has the worldwide universities and uk_universities has a list of 90 universities in the UK


#Importing Plotly libraries to run the below code to plot the map
import plotly.graph_objects as go
import pandas as pd
import plotly.offline as pyo
# Code Create the choropleth map referred from the week 8 code bits
fig = go.Figure(data=go.Choropleth(
    locations=df['Location'],
    z=df['2024 RANK'],
    locationmode='country names',
    colorscale='Viridis',
    colorbar_title='2024 RANK'
))
fig.update_layout(
    title='2024 RANK Distribution of universities by Country',
    geo=dict(
        showframe=False,
        projection_type='equirectangular'
    )
)
#Printing the map
fig.show()

#The above code is written about https://plotly.com/python/choropleth-maps/


#Importing all the required libraries to run the below code
import pandas as pd
import folium
from geopy.geocoders import Nominatim

# Filter for institutions in the UK
uk_institutions = df[df['Location'] == 'United Kingdom']
# Initialize the Nominatim geocoder
geolocator = Nominatim(user_agent="my_application")
# Create a base map
uk_map = folium.Map(location=[54.5, -6], zoom_start=6)
# Add markers for each institution
for index, row in uk_institutions.iterrows():
    # Get the institution's city
    institution_city = row['Institution City']
    # Geocode the institution's city
    location = geolocator.geocode(institution_city)
    # If a location is found, add a marker
    if location:
        folium.Marker(
            location=[location.latitude, location.longitude],
            tooltip=f"{row['Institution Name']}, {institution_city}",
            icon=folium.Icon(color='green')
        ).add_to(uk_map)
# Display the map
uk_map

#The above code is written in reference to https://geopy.readthedocs.io/en/stable/


#Importing all the required libraries to run the below code
import matplotlib.pyplot as plt
import numpy as np

# Selecting the ranking factors
factors = ['Academic Reputation RANK', 'Employer Reputation RANK', 'Faculty Student RANK',
           'International Faculty RANK', 'International Students RANK', 'International Research Network RANK',
           'Employment Outcomes RANK']

# Creates the radar chart
labels = np.linspace(start=0, stop=2*np.pi, num=len(factors), endpoint=False)
values = uk_universities[factors].values[0]  # Assumeing only one row in the data for simplicity

fig = plt.figure(figsize=(8, 8))
ax = fig.add_subplot(111, polar=True)
ax.set_theta_offset(np.pi / 2)  # To Rotate the chart by 90 degrees
ax.set_rlabel_position(30)  # To Move radial labels away from plotted line
ax.set_xticks(labels)
ax.set_xticklabels(factors)
ax.plot(labels, values, 'bo-', linewidth=2)
ax.fill(labels, values, alpha=0.1)
ax.set_title('Ranking Factors for Institution', pad=30)

plt.show()

#The above code is written in reference to https://matplotlib.org/stable/gallery/pie_and_polar_charts/polar_demo.html and Week 4 code reference


#Importing all the required libraries to run the below code
import plotly.graph_objects as go
import pandas as pd

# Create the histogram
fig = go.Figure(data=[go.Histogram(x= uk_universities['SIZE'], nbinsx=20, text=uk_universities['SIZE'].value_counts().sort_index())])
# Customize the plot layout
fig.update_layout(
    title='Distribution of Institution Sizes',
    xaxis_title='Overall Score',
    yaxis_title='Count',
    bargap=0.1,  # Adjust the gap between bars
)
# Show the plot
fig.show()

#The above code is written in reference to https://plotly.com/python/histograms/ and Week 3 Code reference


#Importing all the required libraries to run the below code
import plotly.graph_objects as go
import pandas as pd
import plotly.offline as pyo

# To Create the bar chart
fig = go.Figure(go.Bar(
    y= uk_universities['Overall SCORE'],
    text= uk_universities['Institution Name']  # Display institution names as text
))
fig.update_layout(
    xaxis_tickangle=-45,  # Rotate x-axis labels
    yaxis_title='Overall Score',
    title='Overall Scores Across Institutions',
    height=600  # Adjust the height of the figure
)
#To display the bar graph
fig.show()

#The above code is written in reference to Week 4 code snips and https://plotly.com/python/bar-charts/


#Importing all the required libraries to run the below code
import pandas as pd
from wordcloud import WordCloud
# Grouping the universities by city
city_groups = uk_universities.groupby('Institution City')['Institution Name'].apply(list)
# To Create a dictionary with city names and university counts
city_counts = {city: len(universities) for city, universities in city_groups.items()}
# To Generate the word cloud
wordcloud = WordCloud().generate_from_frequencies(city_counts)
# To Display the word cloud
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.show()

#The above code is in reference to https://www.datacamp.com/tutorial/wordcloud-python and Week 5 code reference


#Importing all the required libraries to run the below code
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt

# To Create the correlation matrix
corr_matrix = uk_universities[['Academic Reputation SCORE', 'Employer Reputation SCORE', 'International Faculty SCORE',
                    'International Students SCORE', 'Employment Outcomes SCORE']].corr()

# To Create the heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Heatmap of Ranking Factors')
plt.show()

#The above code is written in Reference to https://www.kaggle.com/code/vanooshenzr/exploring-university-rankings-their-performane

	2024 RANK	2023 RANK	Institution Name	Institution City	Location	SIZE	STATUS	Academic Reputation SCORE	Academic Reputation RANK	Employer Reputation SCORE	...	Faculty Student RANK	International Faculty SCORE	International Faculty RANK	International Students SCORE	International Students RANK	International Research Network SCORE	International Research Network RANK	Employment Outcomes SCORE	Employment Outcomes RANK	Overall SCORE
0	2	2	University of Cambridge	Cambridge	United Kingdom	L	A	100.0	3	100.0	...	14	100.0	64	95.8	85	99.9	7	100.0	6	99.2
1	3	4	University of Oxford	Oxford	United Kingdom	L	A	100.0	2	100.0	...	8	98.2	110	98.2	60	100.0	1	100.0	3	98.9
4	6	6=	Imperial College London	London	United Kingdom	L	A	98.3	23	99.4	...	42	100.0	61	100.0	14	96.7	35	83.0	45	97.8
7	9	8	UCL	London	United Kingdom	XL	A	99.5	13	97.9	...	43	99.1	96	100.0	15	100.0	2	51.6	126	92.4
20	22	15	The University of Edinburgh	Edinburgh	United Kingdom	XL	A	98.1	25	96.9	...	176	98.8	100	99.9	33	99.6	12	48.1	141	86.1

2024's QS Ranking Visual Landscape for UK Universities¶